Ecological restoration area intelligent identification method based on multi-source data and model integration

Abstract

The invention relates to the technical field of ecological restoration, and discloses an ecological restoration area intelligent identification method based on multi-source data and model integration. The method comprises the following steps: acquiring multi-source original data, and performing cleaning verification to obtain standard format ecological data; the method comprises the following steps: performing space-time grid division and feature extraction on standard data to form a grid feature sequence containing ecological elements and industrial activity features; performing restoration potential evaluation on the sequence by using a pre-constructed ecological restoration knowledge graph to generate a preliminary restoration region set; and inputting the preliminary result into a dynamic identification model, and outputting an optimized repair strategy set by the model through an iterative optimization process of repair strategy generation and benefit simulation. And finally, completing region boundary precision and priority ranking based on the strategy set, and generating a final ecological restoration region list and a matching strategy. According to the invention, the recognition accuracy of the ecological restoration area and the adaptability of the restoration strategy are improved.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology, specifically to an intelligent identification method for ecological restoration areas based on multi-source data and model integration. Background Technology

[0002] Currently, identifying areas requiring ecological restoration primarily relies on remote sensing image interpretation combined with limited field surveys. This method typically uses a single ecological indicator, such as vegetation cover or water pollution level, to determine a threshold. Common techniques include spatial overlay analysis using GIS software or constructing simple linear evaluation models to assign fixed weights to various ecological factors, calculate scores, and delineate potential restoration areas based on the final scores. These approaches largely depend on prior expert knowledge to determine evaluation factors and weighting systems.

[0003] Existing technical solutions have shortcomings. Assessment models based on static indicators and fixed weights struggle to accurately reflect the complex nonlinear interactions among elements within an ecosystem, as well as the dynamic pressures from human industrial activities. Model assessments are often one-off, unable to prospectively simulate the cascading effects of proposed restoration measures. This leads to recommended restoration areas not being the optimal choice, or the established restoration strategies lacking adaptability. Due to the failure to deeply analyze the intrinsic link between ecological degradation and industrial activities, existing methods often overlook the sustained impact of economic activities on ecosystems when identifying restoration potential, making restoration effects unsustainable and potentially leading to re-degradation after restoration. Furthermore, static assessment frameworks cannot adapt to the natural changes in ecosystems over time and the dynamic responses to human intervention, reducing the scientific rigor and practicality of the identification results. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent identification method for ecological restoration areas based on multi-source data and model integration, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides an intelligent identification method for ecological restoration areas based on multi-source data and model integration, the method comprising: Obtain the original multi-source dataset of the ecological restoration area; Data cleaning and consistency verification are performed on the original multi-source dataset to generate standard format ecological data; Spatiotemporal gridding and feature extraction are performed on standard format ecological data to form a grid feature sequence containing ecological element features and industrial activity features; Based on grid feature sequences, a preliminary set of restoration areas is generated by assessing restoration potential through a pre-constructed ecological restoration knowledge graph. The initial set of repair areas and the grid feature sequence are input into the dynamic identification model. The dynamic identification model outputs an optimized set of repair strategies through an iterative process that integrates repair strategy generation and benefit simulation. Based on the optimized restoration strategy set, the boundaries of the preliminary restoration area set are refined and prioritized to generate the final list of ecological restoration areas and their supporting restoration strategies.

[0006] Preferably, the original multi-source dataset is cleaned and its consistency is verified to generate standard format ecological data, including: Outliers and missing values ​​in the original multi-source dataset are identified and filled and corrected using a data insufflation method based on the proximity of the spatiotemporal context. The filled and corrected data are uniformly converted to the preset spatiotemporal coordinate system and data format; Data in a unified coordinate system is normalized to generate standard format ecological data.

[0007] Preferably, spatiotemporal gridding and feature extraction are performed on standard format ecological data to form a grid feature sequence containing ecological element features and industrial activity features, including: The standard format ecological data is divided into several spatiotemporal grid units according to the preset spatiotemporal resolution; For each spatiotemporal grid unit, quantitative feature indicators are extracted from the dimensions of ecological elements and industrial activities, respectively. All quantized feature indices of each spatiotemporal grid cell are combined into a feature vector of that spatiotemporal grid cell, and the feature vectors of all spatiotemporal grid cells are arranged in spatiotemporal order to form a grid feature sequence.

[0008] Preferably, based on grid feature sequences, a pre-constructed ecological restoration knowledge graph is used to assess restoration potential and generate a preliminary set of restoration areas, including: Each feature vector in the grid feature sequence is semantically matched and its correlation is calculated with the nodes in the ecological restoration knowledge graph. For each spatiotemporal grid unit, based on the correlation calculation results, the applicable restoration mode and constraints are retrieved from the ecological restoration knowledge graph; Based on the retrieved repair patterns and constraints, calculate the comprehensive repair potential score for each spatiotemporal grid cell; Spatiotemporal grid cells with a comprehensive score of repair potential exceeding a preset threshold are selected and spatially adjacent cells are merged to generate a preliminary set of repair regions.

[0009] Preferably, the initial set of repaired regions and the grid feature sequence are input into a dynamic identification model. This dynamic identification model, through an iterative process that integrates repair strategy generation and benefit simulation, outputs an optimized set of repair strategies, including: The dynamic identification model generates one or more candidate repair strategies based on the grid feature sequence fragments of each region in the initial repair region set; For each candidate remediation strategy, the dynamic identification model simulates its evolution trajectory on ecological elements and industrial activities within a preset time scale; Based on the evolution trajectory obtained from the simulation, the long-term comprehensive benefit estimate of each candidate repair strategy is calculated; Based on long-term comprehensive benefit valuation, a strategy optimization mechanism is adopted to iteratively adjust and update candidate repair strategies until the convergence condition is met, and an optimized repair strategy set is output.

[0010] Preferably, the dynamic identification model generates one or more candidate repair strategies based on the grid feature sequence fragments of each region in the initial repair region set, including: By analyzing grid feature sequence fragments, key ecological constraints and industrial synergy opportunities can be identified; By combining a predefined library of restoration measures, a combination of restoration technologies targeting key ecological constraints is matched. Design an industrial operation adjustment plan that incorporates opportunities for industrial synergy; By combining repair technologies with industry operation adjustment plans, candidate repair strategies can be formed.

[0011] Preferably, for each candidate remediation strategy, the dynamic identification model simulates its evolutionary trajectory on ecological elements and industrial activities over a preset time scale, including: Construct a system dynamics model that reflects the interaction mechanism between ecological elements and industrial activities; Candidate repair strategies are input into the system dynamics model as external intervention variables; The dynamic model of the running system is used to deduce the dynamic process of the changes of ecological element indicators and industrial activity indicators over time under the action of candidate restoration strategies, and to generate evolution trajectories. The construction of a system dynamics model reflecting the interaction mechanism between ecological elements and industrial activities includes: Determine the system boundary, where ecological element indicators include at least one of vegetation coverage, biodiversity index, and soil organic matter content, and industrial activity indicators include at least one of agricultural irrigation water consumption, industrial carbon emission intensity, and tourism infrastructure density. Based on historical time-series data, the Granger causality test method is used to identify the causal relationship chain between ecological element indicators and industrial activity indicators, and a causal loop diagram is drawn to visualize the feedback mechanism. Based on the causal loop diagram, state variables, flow variables, and auxiliary variables are defined, and a set of system dynamic equations is established based on the mathematical relationships between the variables. The rate of change of the state variables is controlled by the flow variables, and the auxiliary variables are used to describe external interventions. The parameters of the system dynamic equation set are calibrated using the least squares method or Bayesian inference, and the simulation accuracy of the model is verified by calculating the Nash-Sutcliff efficiency coefficient. After model validation, the system dynamics model is integrated into the dynamic identification model to simulate the long-term impact of candidate repair strategies.

[0012] Preferably, based on the evolution trajectory obtained from the simulation, the long-term comprehensive benefit estimate of each candidate remediation strategy is calculated, including: Extract ecological status and industrial economic indicators from the end of a preset time scale from the evolution trajectory; Ecosystem service value assessment methods are used to convert ecological status index values ​​into quantitative values ​​of ecological benefits. Cost-benefit analysis is used to convert industrial economic indicators into quantifiable economic benefits. The quantitative values ​​of ecological benefits and economic benefits are weighted and integrated to generate a long-term comprehensive benefit valuation.

[0013] Preferably, based on long-term comprehensive benefit estimation, a strategy optimization mechanism is used to iteratively adjust and update candidate repair strategies until convergence conditions are met, including: Compare the long-term overall benefit estimates of all candidate repair strategies in the current iteration; Eliminate some candidate repair strategies that rank low in terms of long-term comprehensive benefit valuation; The retained candidate repair strategies are fine-tuned in terms of parameters or restructured in terms of structure to generate a new generation of candidate repair strategies; Repeat the process from strategy simulation to generating a new generation of candidate repair strategies until the long-term comprehensive benefit estimate of the best candidate repair strategy no longer increases significantly after multiple iterations, then the convergence condition is met.

[0014] Preferably, the preliminary set of restoration areas is refined in terms of boundaries and prioritized based on the optimized restoration strategy set, generating a final list of ecological restoration areas and their corresponding restoration strategies, including: Map each optimized repair strategy in the optimized repair strategy set back to its corresponding initial repair area; Based on the engineering implementation scope implied by the optimized repair strategy, the spatial boundaries of the preliminary repair area are precisely defined. Based on the long-term comprehensive benefit assessment of the optimized restoration strategy, priority values ​​are assigned to the ecological restoration areas after detailed surveying; By integrating all ecological restoration areas that have undergone boundary refinement and priority assignment, along with their corresponding optimized restoration strategies, a final list of ecological restoration areas is generated.

[0015] Compared with the prior art, the beneficial effects of the present invention are: Ecological restoration potential is assessed through a pre-constructed ecological restoration knowledge graph, which structurally integrates multi-source, heterogeneous ecological elements, industrial activity parameters, and their complex semantic relationships. Entities in the grid feature sequence are dynamically associated and mapped with nodes in the knowledge graph. Based on pre-defined rules and reasoning paths within the graph, the intrinsic causal relationships between ecological degradation and surrounding industrial activities and natural geographical conditions can be deeply explored. This allows the assessment to go beyond surface features and better understand the driving mechanisms behind ecological problems, thereby identifying areas significantly affected by human activities but from which ecological gains can be achieved through intervention, thus enhancing the depth and interpretability of the potential assessment.

[0016] This paper employs a dynamic identification model that integrates the iterative process of remediation strategy generation and benefit simulation. The model treats strategy generation as a multi-objective optimization problem and, in each iteration, invokes a benefit simulator to quantify the potential effects of different strategies across ecological, economic, and other dimensions. Simulation results serve as feedback information, guiding real-time strategy adjustment and optimization, forming a closed-loop decision support process. This method overcomes the limitation of static models having fixed outputs, automatically exploring and approximating a near-optimal solution set under given constraints. It ensures that the final output remediation strategy set is not only based on the current state but also fully considers the long-term dynamic evolution and comprehensive benefits after strategy implementation, improving the scientific rigor, adaptability, and operability of the remediation scheme. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent identification method for ecological restoration areas based on multi-source data and model integration described in this invention. Figure 2 This is a flowchart of data cleaning and consistency verification. Figure 3 A flowchart for assessing repair potential; Figure 4 A diagram showing the coordination degree of the repair strategy; Figure 5 This is a chart showing the long-term evolution trend of multiple indicators. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1This invention provides an intelligent identification method for ecological restoration areas based on multi-source data and model integration. The method includes: acquiring the original multi-source dataset of ecological restoration areas; cleaning and verifying the consistency of the original multi-source dataset to generate standard format ecological data; performing spatiotemporal grid division and feature extraction on the standard format ecological data to form a grid feature sequence containing ecological element features and industrial activity features; evaluating the restoration potential based on the grid feature sequence using a pre-constructed ecological restoration knowledge graph to generate a preliminary set of restoration areas; inputting the preliminary set of restoration areas and the grid feature sequence into a dynamic identification model; outputting an optimized set of restoration strategies through an iterative process that integrates restoration strategy generation and benefit simulation; and refining the boundaries and prioritizing the preliminary set of restoration areas according to the optimized set of restoration strategies to generate a final list of ecological restoration areas and their corresponding restoration strategies.

[0020] Example 1: See Figure 2 In practical implementation, an intelligent identification method for ecological restoration areas based on multi-source data and model integration involves data cleaning and consistency verification of the original multi-source dataset to generate standard-format ecological data. The original multi-source dataset includes remote sensing images, ground monitoring records, and industry statistical reports. Outliers and missing values ​​in the original multi-source dataset are identified using a data inpainting method based on proximity spatiotemporal context. This method estimates reasonable values ​​by analyzing valid data points within the spatiotemporal neighborhood of the missing value. For example, for a missing data point at a certain spatiotemporal location, the inpainted value is calculated based on the weighted average of neighboring valid data points, with the weights determined by the spatiotemporal distance. The inpainting formula is expressed as: in: This represents the estimation results for missing values. Indicates the number of neighboring valid data points. Indicates the first The spatiotemporal weighting coefficient for each valid data point is inversely proportional to the spatiotemporal distance. Indicates the first The observations of each valid data point were used. The filled and corrected data were then uniformly converted to a preset spatiotemporal coordinate system and data format. The preset spatiotemporal coordinate system adopted a common geographic coordinate system and standard timestamps, and the data format was standardized as structured tables or raster data. The data under the unified coordinate system underwent dimensional normalization processing, and the minimum-maximum scaling method was used to convert indicators of different dimensions to the [0,1] interval, generating standard format ecological data.

[0021] In some embodiments, standard-format ecological data is divided into several spatiotemporal grid units according to a preset spatiotemporal resolution. The preset spatiotemporal resolution is determined based on the scale of the study area and the data accuracy; for example, the spatial resolution is set to a 1-kilometer grid, and the temporal resolution is set to monthly aggregation. Quantitative feature indicators are extracted from both ecological element and industrial activity dimensions for each spatiotemporal grid unit. The ecological element dimension includes vegetation index, soil moisture, and biodiversity index; the industrial activity dimension includes agricultural fertilizer use, industrial wastewater discharge intensity, and tourist density. All quantitative feature indicators of each spatiotemporal grid unit are combined to form a feature vector for that unit. The feature vector is a multi-dimensional numerical array. Arranging the feature vectors of all spatiotemporal grid units in spatiotemporal order forms a grid feature sequence, with timestamps and spatial coordinates as index keys.

[0022] Optionally, data restoration methods based on proximity spatiotemporal context employ statistical outlier detection algorithms when identifying outliers, such as using the Z-score method to mark observations exceeding three standard deviations as outliers. It is understood that dimensional normalization also includes one-hot encoding transformation of categorical data to ensure all features are of the same magnitude. In some embodiments, spatiotemporal grid partitioning employs regular grids or adaptive grid methods; regular grids divide the region into uniformly sized cells, while adaptive grids dynamically adjust the grid size according to data density. Optionally, the feature extraction process includes calculating statistical features such as mean, variance, and temporal trend slope to capture dynamic changes in ecological elements and industrial activities.

[0023] Example 2: See Figure 3 In practical implementation, a preliminary set of restoration areas is generated by assessing restoration potential based on a pre-constructed ecological restoration knowledge graph using grid feature sequences. The grid feature sequences contain ecological element features and industrial activity feature vectors of spatiotemporal grid units, while the pre-constructed ecological restoration knowledge graph includes ecological entities, restoration technologies, and environmental constraints. Each feature vector in the grid feature sequence is semantically matched and its relevance is calculated with nodes in the ecological restoration knowledge graph. Semantic matching is achieved by calculating the cosine similarity between the feature vector and the embedding vector of the knowledge graph node. Relevance calculation comprehensively considers semantic similarity and topological proximity. A relevance score is then generated. The calculation formula is expressed as: in: This represents the final relevance score. The weight coefficients representing semantic similarity range from 0 to 1. This represents the semantic similarity score calculated using cosine similarity. This represents the topological proximity score calculated based on the reciprocal of the shortest path between nodes in the knowledge graph. For each spatiotemporal grid cell, applicable restoration modes and constraints are retrieved from the ecological restoration knowledge graph based on its correlation calculation results. The correlation calculation results refer to the restoration modes and constraints linked to knowledge graph nodes whose correlation scores exceed a preset threshold.

[0024] In some embodiments, a comprehensive restoration potential score for each spatiotemporal grid cell is calculated based on the retrieved restoration patterns and constraints. This score is obtained by weighted summation of multiple normalized evaluation indicators, including the potential for improving ecological sensitivity and the feasibility of adjusting industrial activities. Spatiotemporal grid cells with comprehensive restoration potential scores exceeding a preset threshold are selected and spatially adjacent cells are merged to generate a preliminary set of restoration regions. The preset threshold is determined by the quantiles of historical data statistical distributions. Merging spatially adjacent cells employs a region growing algorithm based on eight-neighborhood connectivity. Specifically, the region growing algorithm based on eight-neighborhood connectivity is used to merge spatially adjacent spatiotemporal grid cells to generate a preliminary set of restoration regions. The algorithm begins by marking spatiotemporal grid cells with comprehensive restoration potential scores exceeding the preset threshold as seed points. The preset threshold is determined by the quantiles of historical data statistical distributions to ensure the statistical rationality of the selection. The region growing process uses these seed points as initial growth regions. The algorithm checks the eight neighboring grid cells of each seed point to determine if these neighboring cells also meet the condition of having a comprehensive restoration potential score exceeding the preset threshold. If a neighboring cell meets the condition, it is merged into the current growth region to form a larger contiguous region.

[0025] Optionally, the semantic matching process employs a semantic matching method based on word vectors and ontology hierarchy, comparing the similarity between the text labels of the feature vectors and the labels of the knowledge graph nodes. It is understood that the weight coefficient α in the correlation calculation is optimized and determined through grid search combined with cross-validation. In some embodiments, when retrieving restoration patterns and constraints from the ecological restoration knowledge graph, a graph database query language is used to traverse associated nodes and their relational edges. Optionally, the calculation of the comprehensive restoration potential score introduces a spatial autocorrelation correction term to consider the mutual influence of neighboring grid cells. It is understood that after merging spatially adjacent cells, the boundary of the initial restoration region set is smoothed through morphological closing operations.

[0026] Example 3: In specific implementation, the dynamic identification model generates one or more candidate restoration strategies based on the grid feature sequence fragments of each region in the preliminary restoration area set. These grid feature sequence fragments contain the spatiotemporal grid cell feature vector sequences of the target region and its surrounding buffer zone. The grid feature sequence fragments are analyzed to identify key ecological constraints and industrial synergy opportunities. The analysis process uses a combination of principal component analysis and random forest feature importance ranking. Key ecological constraints are represented by feature indicators with the largest deviation from the ideal ecological state, while industrial synergy opportunities are nodes where industrial activity characteristics are positively correlated with ecological restoration needs. A predefined restoration measures library is used to match restoration technology combinations targeting key ecological constraints. This library, stored in the form of a knowledge graph, includes various restoration technologies such as vegetation restoration, soil improvement, and water resource regulation, along with their applicable conditions. The matching process is based on a constraint satisfaction problem-solving algorithm, selecting technology combinations that simultaneously meet technical feasibility and regional applicability. An industrial operation adjustment plan incorporating industrial synergy opportunities is designed, including specific measures for production process optimization and resource recycling model adjustments. By binding the combination of remediation technologies with industrial operation adjustment plans, candidate remediation strategies are formed. The binding operation establishes the logical connection and temporal dependency between remediation actions and industrial adjustments.

[0027] In some embodiments, for each candidate restoration strategy, a dynamic identification model simulates its evolution trajectory on ecological elements and industrial activities over a preset time scale, constructing a system dynamics model reflecting the interaction mechanism between ecological elements and industrial activities. The system dynamics model includes multiple stock variables, flow variables, and auxiliary variables, with relationships between variables defined through differential equations and table functions. Candidate restoration strategies are input into the system dynamics model as external intervention variables, which characterize the implementation intensity of restoration projects and the pace of industrial adjustment in time series form. The system dynamics model is run to deduce the dynamic process of ecological element indicators and industrial activity indicators changing over time under the influence of candidate restoration strategies, generating evolution trajectories. The deduction process uses the fourth-order Runge-Kutta method to numerically integrate and solve the system of differential equations.

[0028] Optionally, a sliding time window analysis technique is used when analyzing the grid feature sequence fragments to capture the temporal variation patterns of ecological constraints. It is understood that the matching process of the restoration measure library incorporates multi-objective optimization, simultaneously balancing technical costs, implementation difficulty, and expected ecological benefits. In some embodiments, the design of industrial operation adjustment schemes adopts an agent-based modeling method to simulate the response behavior of microeconomic agents to policy interventions. Optionally, the construction of the system dynamics model incorporates a delay function and a random disturbance term to better reflect the dynamic complexity of the ecological economic system. It is understood that the generation of the evolution trajectory includes a Monte Carlo simulation step to assess the uncertainty range of the strategy implementation effect. The simulation evaluation framework for candidate restoration strategies uses the following formula to calculate the coordination degree of the strategies: in: An index representing the degree of coordination between ecological restoration and industrial activities. Indicates the quantity of ecological element indicators. Indicates the quantity of industry activity indicators, This represents the normalization deviation of the i-th ecological element indicator. This represents the normalization deviation of the j-th industry activity indicator.

[0029] See Figure 4 This chart presents a comprehensive evaluation of five different types of ecological restoration strategies. Each strategy category is represented by two parallel bars, indicating its performance scores across two dimensions: coordination index and ecological benefits. By comparing the differences in bar heights for different strategies, the relative strengths and weaknesses of each strategy in coordinating ecological restoration with industrial activities and improving ecological benefits are clearly visible. Different colors are used to distinguish the two evaluation dimensions, along with clear data labels, enabling readers to quickly identify the best-performing restoration strategy. The chart's overall layout is clear and the colors are harmonious, providing an important reference for selecting ecological restoration strategies through intuitive visual comparison.

[0030] Example 4: In specific implementation, constructing a system dynamics model reflecting the interaction mechanism between ecological elements and industrial activities requires determining the system boundary. Ecological element indicators include at least one of vegetation cover, biodiversity index, and soil organic matter content; industrial activity indicators include at least one of agricultural irrigation water consumption, industrial carbon emission intensity, and tourism infrastructure density. Based on historical time-series data, the Granger causality test method is used to identify the causal relationship chain between ecological element indicators and industrial activity indicators. The Granger causality test determines the lead-lag relationship between variables by constructing a vector autoregression model and testing the significance of the coefficients. For causal relationships that meet the significance level, a causal loop diagram is drawn to visualize the feedback mechanism. The causal loop diagram includes two basic types: reinforcing loops and regulating loops. According to the causal loop diagram, state variables, flow variables, and auxiliary variables are defined. State variables represent the cumulative quantity of the system, such as the total vegetation biomass; flow variables represent the rate of change of state variables, such as annual carbon sequestration; and auxiliary variables are used to describe external interventions, such as the investment intensity of restoration projects. A set of system dynamic equations is established based on the mathematical relationships between variables, and the rate of change of state variables is controlled by flow variables.

[0031] In some embodiments, the parameters of the system dynamics equation set are calibrated using the least squares method or Bayesian inference. The least squares method is suitable for equations with explicit linear relationships, while Bayesian inference is suitable for cases where the parameters have a prior distribution. The simulation accuracy of the model is verified by calculating the Nash-Sutcliffe efficiency coefficient. The formula for calculating the Nash-Sutcliffe efficiency coefficient is as follows: in: This represents the Nash-Sutcliffe efficiency coefficient. Indicates the total length of the time series. Indicates the first Observations at each time point Indicates the first Simulated values ​​at each time point This represents the average value of the observed values. After model validation, the system dynamics model is integrated into the dynamic identification model to simulate the long-term impact of candidate remediation strategies. Based on the evolution trajectory obtained from the simulation, the long-term comprehensive benefit estimate of each candidate remediation strategy is calculated, and the ecological state index value and industrial economic index value at the end of the preset time scale are extracted from the evolution trajectory.

[0032] Optionally, the ecological service value assessment method uses the unit service equivalent factor value method to quantify ecological benefits, while the cost-benefit analysis method uses the net present value method to quantify economic benefits. It can be understood that the weighted fusion process uses a linear weighted sum model, and the weight coefficients are determined through the analytic hierarchy process combined with expert scoring. Refer to Table 1, which presents a quantitative indicator system for ecological and economic benefits.

[0033] Table 1: Quantitative Indicators of Ecological and Economic Benefits

[0034] In some embodiments, the calculation of long-term comprehensive benefit valuation considers time discounting factors, uniformly discounting benefit flows at different time points to the base year. Optionally, the quantification of ecological benefits includes the estimation of non-use value, using conditional valuation to quantify existence value and heritage value. It is understood that the benefit values ​​before weighted fusion need to be normalized to eliminate the influence of dimensions, and the normalization method uses the range method to convert to the [0,1] interval.

[0035] See Figure 5 This chart employs a multi-axis design to display the long-term dynamic evolution of several key indicators in the eco-industry system on the same time scale. Using the time axis as a baseline, different curve styles represent the changing trends of indicators such as vegetation biomass, agricultural output, and carbon emission intensity. Vertical dashed lines mark the starting points of ecological restoration interventions, clearly delineating the two stages of natural evolution and human intervention. The changes in each curve before and after intervention vividly reflect the comprehensive impact of restoration measures on the system. The chart also indicates the efficiency coefficients of the model validation, demonstrating the reliability of the simulation results. Overall, this chart, through the integrated display of multiple indicators, reveals the dynamic response patterns of complex eco-economic systems and the long-term effects of restoration measures.

[0036] Example 5: In specific implementation, a strategy optimization mechanism is used to iteratively adjust and update candidate restoration strategies based on long-term comprehensive benefit valuation until convergence conditions are met. The long-term comprehensive benefit valuations of all candidate restoration strategies in the current iteration are compared. These valuations are calculated by simulating the evolution trajectory of the candidate restoration strategies. Candidate restoration strategies with lower long-term comprehensive benefit valuations are eliminated, with the elimination ratio adaptively adjusted according to the number of iterations. The remaining candidate restoration strategies undergo parameter fine-tuning or structural reorganization to generate a new generation of candidate restoration strategies. Parameter fine-tuning involves modifying implementation parameters of the restoration technology combination, such as vegetation restoration density or irrigation frequency. Structural reorganization involves changing the logical connection between restoration measures and industrial adjustment plans. The process from strategy simulation to generating a new generation of candidate restoration strategies is repeated. Strategy simulation refers to inputting candidate restoration strategies into the system dynamics model to deduce the dynamic changes of ecological elements and industrial activities. Generating a new generation of candidate restoration strategies refers to generating new strategies through cross-mutation operations. The convergence condition is met when the long-term comprehensive benefit valuation of the best candidate restoration strategy no longer significantly improves after multiple iterations. The convergence determination formula is expressed as: in: Indicates the relative rate of change. This represents the optimal long-term overall benefit estimate in the iterth iteration. This represents the optimal long-term comprehensive benefit estimate for the (iter-1)th iteration, when the sum of k consecutive iterations... When all values ​​are less than the preset threshold, the convergence condition is considered met.

[0037] In some embodiments, the elimination operation employs an elite retention strategy, retaining the top 20% of candidate repair strategies with the highest long-term comprehensive benefit valuations into the next generation. It can be understood that the parameter fine-tuning process introduces Gaussian perturbations to explore new regions of the parameter space.

[0038] In practice, the preliminary restoration area set is refined and prioritized based on the optimized restoration strategy set to generate a final list of ecological restoration areas and their corresponding restoration strategies. Each optimized restoration strategy in the set is mapped back to its corresponding preliminary restoration area. The mapping process is based on spatial location coding matching strategies and the geographic identifiers of the areas. The spatial boundaries of the preliminary restoration areas are refined based on the engineering implementation scope implied by the optimized restoration strategies. The engineering implementation scope includes the core area and the impact buffer zone of the restoration project. The refined determination employs geographic information system overlay analysis and boundary smoothing algorithms. The ecological restoration areas after refined determination are prioritized based on the long-term comprehensive benefit estimates of the optimized restoration strategies. The priority assignment uses a normalized scoring method to convert the long-term comprehensive benefit estimates into priority scores. All ecological restoration areas that have undergone boundary refinement and priority assignment, along with their corresponding optimized restoration strategies, are integrated to generate a final list of ecological restoration areas. This final list is stored in the form of spatial vector data and attribute tables.

[0039] In some embodiments, refined boundary delineation incorporates topographic constraints and current land use data as correction factors. Optionally, priority assignment combines time urgency and resource availability for multi-criteria decision-making. It is understood that the output format of the final ecological restoration area list supports seamless integration with the planning management platform.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent identification of ecological restoration areas based on multi-source data and model integration, characterized in that, The method includes: Obtain the original multi-source dataset of the ecological restoration area; Data cleaning and consistency verification are performed on the original multi-source dataset to generate standard format ecological data; Spatiotemporal gridding and feature extraction are performed on standard format ecological data to form a grid feature sequence containing ecological element features and industrial activity features; Based on grid feature sequences, a preliminary set of restoration areas is generated by assessing restoration potential through a pre-constructed ecological restoration knowledge graph. The initial set of repair areas and the grid feature sequence are input into the dynamic identification model. The dynamic identification model outputs an optimized set of repair strategies through an iterative process that integrates repair strategy generation and benefit simulation. Based on the optimized restoration strategy set, the boundaries of the preliminary restoration area set are refined and prioritized to generate the final list of ecological restoration areas and their supporting restoration strategies.

2. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 1, characterized in that, Data cleaning and consistency verification are performed on the original multi-source datasets to generate standard-format ecological data, including: Outliers and missing values ​​in the original multi-source dataset are identified and filled and corrected using a data insufflation method based on the proximity of the spatiotemporal context. The filled and corrected data are uniformly converted to the preset spatiotemporal coordinate system and data format; Data in a unified coordinate system is normalized to generate standard format ecological data.

3. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 2, characterized in that, Spatiotemporal gridding and feature extraction are performed on standard format ecological data to form a grid feature sequence containing ecological element features and industrial activity features, including: The standard format ecological data is divided into several spatiotemporal grid units according to the preset spatiotemporal resolution; For each spatiotemporal grid unit, quantitative feature indicators are extracted from the dimensions of ecological elements and industrial activities, respectively. All quantized feature indices of each spatiotemporal grid cell are combined into a feature vector of that spatiotemporal grid cell, and the feature vectors of all spatiotemporal grid cells are arranged in spatiotemporal order to form a grid feature sequence.

4. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 3, characterized in that, Based on grid feature sequences, a pre-constructed ecological restoration knowledge graph is used to assess restoration potential and generate a preliminary set of restoration areas, including: Each feature vector in the grid feature sequence is semantically matched and its correlation is calculated with the nodes in the ecological restoration knowledge graph. For each spatiotemporal grid unit, based on the correlation calculation results, the applicable restoration mode and constraints are retrieved from the ecological restoration knowledge graph; Based on the retrieved repair patterns and constraints, calculate the comprehensive repair potential score for each spatiotemporal grid cell; Spatiotemporal grid cells with a comprehensive score of repair potential exceeding a preset threshold are selected and spatially adjacent cells are merged to generate a preliminary set of repair regions.

5. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 4, characterized in that, The initial set of repairable areas and the grid feature sequence are input into a dynamic identification model. This model, through an iterative process that integrates repair strategy generation and benefit simulation, outputs an optimized set of repair strategies, including: The dynamic identification model generates one or more candidate repair strategies based on the grid feature sequence fragments of each region in the initial repair region set; For each candidate remediation strategy, the dynamic identification model simulates its evolution trajectory on ecological elements and industrial activities within a preset time scale; Based on the evolution trajectory obtained from the simulation, the long-term comprehensive benefit estimate of each candidate repair strategy is calculated; Based on long-term comprehensive benefit valuation, a strategy optimization mechanism is adopted to iteratively adjust and update candidate repair strategies until the convergence condition is met, and an optimized repair strategy set is output.

6. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 5, characterized in that, The dynamic identification model generates one or more candidate repair strategies based on the grid feature sequence fragments of each region in the initial repair region set, including: By analyzing grid feature sequence fragments, key ecological constraints and industrial synergy opportunities can be identified; By combining a predefined library of restoration measures, a combination of restoration technologies targeting key ecological constraints is matched. Design an industrial operation adjustment plan that incorporates opportunities for industrial synergy; By combining repair technologies with industry operation adjustment plans, candidate repair strategies can be formed.

7. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 6, characterized in that, For each candidate remediation strategy, the dynamic identification model simulates its evolutionary trajectory on ecological elements and industrial activities over a preset timescale, including: Construct a system dynamics model that reflects the interaction mechanism between ecological elements and industrial activities; Candidate repair strategies are input into the system dynamics model as external intervention variables; The dynamic model of the running system is used to deduce the dynamic process of the changes of ecological element indicators and industrial activity indicators over time under the action of candidate restoration strategies, and to generate evolution trajectories. The construction of a system dynamics model reflecting the interaction mechanism between ecological elements and industrial activities includes: Determine the system boundary, where ecological element indicators include at least one of vegetation coverage, biodiversity index, and soil organic matter content, and industrial activity indicators include at least one of agricultural irrigation water consumption, industrial carbon emission intensity, and tourism infrastructure density. Based on historical time-series data, the Granger causality test method is used to identify the causal relationship chain between ecological element indicators and industrial activity indicators, and a causal loop diagram is drawn to visualize the feedback mechanism. Based on the causal loop diagram, state variables, flow variables, and auxiliary variables are defined, and a set of system dynamic equations is established based on the mathematical relationships between the variables. The rate of change of the state variables is controlled by the flow variables, and the auxiliary variables are used to describe external interventions. The parameters of the system dynamic equation set are calibrated using the least squares method or Bayesian inference, and the simulation accuracy of the model is verified by calculating the Nash-Sutcliff efficiency coefficient. After model validation, the system dynamics model is integrated into the dynamic identification model to simulate the long-term impact of candidate repair strategies.

8. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 7, characterized in that, Based on the evolution trajectory obtained from the simulation, the long-term comprehensive benefit estimate of each candidate remediation strategy is calculated, including: Extract ecological status and industrial economic indicators from the end of a preset time scale from the evolution trajectory; Ecosystem service value assessment methods are used to convert ecological status index values ​​into quantitative values ​​of ecological benefits. Cost-benefit analysis is used to convert industrial economic indicators into quantifiable economic benefits. The quantitative values ​​of ecological benefits and economic benefits are weighted and integrated to generate a long-term comprehensive benefit valuation.

9. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 8, characterized in that, Based on long-term comprehensive benefit estimation, a strategy optimization mechanism is used to iteratively adjust and update candidate remediation strategies until convergence conditions are met, including: Compare the long-term overall benefit estimates of all candidate repair strategies in the current iteration; Eliminate some candidate repair strategies that rank low in terms of long-term comprehensive benefit valuation; The retained candidate repair strategies are fine-tuned in terms of parameters or restructured in terms of structure to generate a new generation of candidate repair strategies; Repeat the process from strategy simulation to generating a new generation of candidate repair strategies until the long-term comprehensive benefit estimate of the best candidate repair strategy no longer increases significantly after multiple iterations, then the convergence condition is met.

10. The intelligent identification method for ecological restoration areas based on multi-source data and model integration according to claim 9, characterized in that, Based on the optimized restoration strategy set, the preliminary restoration area set is refined in terms of boundary precision and prioritized, generating a final list of ecological restoration areas and their corresponding restoration strategies, including: Map each optimized repair strategy in the optimized repair strategy set back to its corresponding initial repair area; Based on the engineering implementation scope implied by the optimized repair strategy, the spatial boundaries of the preliminary repair area are precisely defined. Based on the long-term comprehensive benefit assessment of the optimized restoration strategy, priority values ​​are assigned to the ecological restoration areas after detailed surveying; By integrating all ecological restoration areas that have undergone boundary refinement and priority assignment, along with their corresponding optimized restoration strategies, a final list of ecological restoration areas is generated.

Images